基于大数据的音乐流行趋势预测及推荐分析毕业论文+项目源码+爬虫源码+网页端源码+数据库sql文件

目录摘要Abstract第1章前言1.1 研究背景1.2 研究现状1.2.1 国内研究现状1.2.2 国外研究现状1.3 发展趋势1.4 研究主要内容第2章技术与原理2.1 大数据环境2.1.1 全球开源的Linux系统-Ubuntu2.1.2 开源分大数据处理平台Hadoop2.2 数据获取与处理2.3网页端第3章数据采集及分析3.1 数据获取3.2 音频处理3.2.1 音乐格式转换与时

2401_84185471

522人浏览 · 2024-05-15 10:00:52

2401_84185471 · 2024-05-15 10:00:52 发布

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音乐流行趋势预测为企业预测未来流行音乐走向、预判黑马音乐提供了理论支撑和决策上的技术支持，具有重要的商业价值。相比传统预测方法，基于机器学习构建预测模型不仅理论完善，而且在实践中有强大的鲁棒性和泛化能力，可直接移植到平台中应用。

Music pop trend prediction and recommendation analysis based on big data

Abstract

The music pop trend prediction model construction based on machine learning uses only the data close to the predicted target time period. In this paper, the grouping experiment is conducted after song clustering: based on the fuzzy set theory, decompose the time information particles and construct the “triangle” model; to predict the low, R and up parameters of the triangle model by SVM, we can get accurate short-time space and trend change. This is an important guiding role in the original behavior, usage behavior and carrier marketing activities on the platform.

The system realizes the collection of music score (Python crawler crawl data), the back end uses the big teaching data recommendation algorithm construction, and the front end uses the MVC framework to build the big data music recommendation system. The systematic reference order uses the relational reference library MySQL. The front end collected user behavior data and passed it to the back end using a user-based collaborative filtering algorithm to recommend music that the user may like. The BS architecture was developed by using the Java programming language, MySQL database, Hadoop offline analysis, java open source tool Eclipse programming, Java’s JRE running environment, JSP page and other tools.

Music pop trend forecast provides theoretical support and decision-making technical support for enterprises to predict the future pop music trend and predict the dark horse music, which has important commercial value. Compared with traditional prediction methods, building prediction models based on machine learning is not only theoretically perfect, but also has strong robustness and generalization ability in practice, which can be directly transplanted to the platform for application.

2.1.1 全球开源的Linux系统-Ubuntu